Development of a chopper with performance optimization for areca nut frond using response surface methodology

Abstract

The chopper's performance is affected by the moisture content level of the material being chopped and the speed of the machine's rotation. This research aims to develop a chopper for an areca nut frond with performance optimization using RSM. RSM was utilized to assess the effect of areca nut frond moisture content (50.50–73.50%, wb) and machine rotation speed (1000–1600 rpm) on capacity, efficiency, chopping width, chopping length, and fuel consumption reactions. Next, the desirability formula is used to discover the moisture content and the machine rotation speed to maximize capacity and efficiency while minimizing losses. The optimum moisture content of 73.50% and machine rotation speed of 1600 rpm was performed in the highest capacity of 126.46 kg/h at more increased efficiency (81.1%) and minimum chopping width and length (2.4 mm, 21.51 mm) and lower fuel consumption (0.91 ml). These findings showed that an appropriate machine performance could be completed by utilizing the moisture content of the areca nut frond and the machine's rotation speed.